Efficiency of Local Genetic Algorithm in Parallel Processing

Authors:
PENG Gang;Ichiro IIMURA;Takeshi NAKATSURU;Shigeru NAKAYAMA
Affiliations:
Oita National College of Technology Maki, Oita City, Japan;Prefectural University of Kumamoto, Tsukide, Kumamoto, Japan;Prefectural University of Kumamoto, Tsukide, Kumamoto, Japan;Kagoshima University, Japan
Venue:
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Year:
2005

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Abstract

This paper discusses a parallel genetic algorithm (GA) which focuses on the local operator for Traveling salesman problem (TSP). The local operator is a simple GA named as Local Genetic Algorithm (LGA). The LGA is combined to another GA named as Global Genetic Algorithm (GGA). It increases the computational time running a GA as a local operator in another one. To solve this problem, we build a parallel system based on our previous works for running the LGA to speed up the process. The results show that LGA improve the search quality significantly and it is more efficient running LGA with parallel system than single CPU.